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DOE OSTI2020

Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models

Abstract Background A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic. Results Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp 614 Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro 323 Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus. Conclusions These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Challenges of COVID-19 Case Forecasting in the US, 2020–2021

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub ( https://covid19forecasthub.org ). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1–4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Synthetic method of analogues for emerging infectious disease forecasting

The Method of Analogues (MOA) has gained popularity in the past decade for infectious disease forecasting due to its non-parametric nature. In MOA, the local behavior observed in a time series is matched to the local behaviors of several historical time series. The known values that directly follow the historical time series that best match the observed time series are used to calculate a forecast. This non-parametric approach leverages historical trends to produce forecasts without extensive parameterization, making it highly adaptable. However, MOA is limited in scenarios where historical data is sparse. This limitation was particularly evident during the early stages of the COVID-19 pandemic, where the emerging global epidemic had little-to-no historical data. In this work, we propose a new method inspired by MOA, called the Synthetic Method of Analogues (sMOA). sMOA replaces historical disease data with a library of synthetic data that describe a broad range of possible disease trends. This model circumvents the need to estimate explicit parameter values by instead matching segments of ongoing time series data to a comprehensive library of synthetically generated segments of time series data. We demonstrate that sMOA has competitive performance with state-of-the-art infectious disease forecasting models, out-performing 78% of models from the COVID-19 Forecasting Hub in terms of averaged Mean Absolute Error and 76% of models from the COVID-19 Forecasting Hub in terms of averaged Weighted Interval Score. Additionally, we introduce a novel uncertainty quantification methodology designed for the onset of emerging epidemics. Developing versatile approaches that do not rely on historical data and can maintain high accuracy in the face of novel pandemics is critical for enhancing public health decision-making and strengthening preparedness for future outbreaks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Fast Evaluation of Viral Emerging Risks (FEVER): A computational tool for biosurveillance, diagnostics, and mutation typing of emerging viral pathogens

Viral pathogens can rapidly evolve, adapt to novel hosts, and evade human immunity. The early detection of emerging viral pathogens through biosurveillance coupled with rapid and accurate diagnostics are required to mitigate global pandemics. However, RNA viruses can mutate rapidly, hampering biosurveillance and diagnostic efforts. Here, we present a novel computational approach called FEVER (Fast Evaluation of Viral Emerging Risks) to design assays that simultaneously accomplish: 1) broad-coverage biosurveillance of an entire group of viruses, 2) accurate diagnosis of an outbreak strain, and 3) mutation typing to detect variants of public health importance. We demonstrate the application of FEVER to generate assays to simultaneously 1) detect sarbecoviruses for biosurveillance; 2) diagnose infections specifically caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); and 3) perform rapid mutation typing of the D614G SARS-CoV-2 spike variant associated with increased pathogen transmissibility. These FEVER assays had a high in silico recall (predicted positive) up to 99.7% of 525,708 SARS-CoV-2 sequences analyzed and displayed sensitivities and specificities as high as 92.4% and 100% respectively when validated in 100 clinical samples. The D614G SARS-CoV-2 spike mutation PCR test was able to identify the single nucleotide identity at position 23,403 in the viral genome of 96.6% SARS-CoV-2 positive samples without the need for sequencing. This study demonstrates the utility of FEVER to design assays for biosurveillance, diagnostics, and mutation typing to rapidly detect, track, and mitigate future outbreaks and pandemics caused by emerging viruses.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Zinc against COVID-19? Symptom surveillance and deficiency risk groups

A wide variety of symptoms is associated with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, and these symptoms can overlap with other conditions and diseases. Knowing the distribution of symptoms across diseases and individuals can support clinical actions on timelines shorter than those for drug and vaccine development. Here, we focus on zinc deficiency symptoms, symptom overlap with other conditions, as well as zinc effects on immune health and mechanistic zinc deficiency risk groups. There are well-studied beneficial effects of zinc on the immune system including a decreased susceptibility to and improved clinical outcomes for infectious pathogens including multiple viruses. Zinc is also an anti-inflammatory and anti-oxidative stress agent, relevant to some severe Coronavirus Disease 2019 (COVID-19) symptoms. Unfortunately, zinc deficiency is common worldwide and not exclusive to the developing world. Lifestyle choices and preexisting conditions alone can result in zinc deficiency, and we compile zinc risk groups based on a review of the literature. It is also important to distinguish chronic zinc deficiency from deficiency acquired upon viral infection and immune response and their different supplementation strategies. Zinc is being considered as prophylactic or adjunct therapy for COVID-19, with 12 clinical trials underway, highlighting the relevance of this trace element for global pandemics. Using the example of zinc, we show that there is a critical need for a deeper understanding of essential trace elements in human health, and the resulting deficiency symptoms and their overlap with other conditions. This knowledge will directly support human immune health for decreasing susceptibility, shortening illness duration, and preventing progression to severe cases in the current and future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Identifying key determinants and dynamics of SARS-CoV-2/ACE2 tight interaction

SARS-CoV-2 virus, the causative agent of Covid-19, has fired up a global pandemic. The virus interacts with the human receptor angiotensin-converting enzyme 2 (ACE2) for an invasion via receptor binding domain (RBD) on its spike protein. To provide a deeper understanding of this interaction, we performed microsecond simulations of the RBD-ACE2 complex for SARS-CoV-2 and compared it with the closely related SARS-CoV discovered in 2003. We show residues in the RBD of SARS-CoV-2 that were mutated from SARS-CoV, collectively help make the RBD anchor much stronger to the N-terminal part of ACE2 than the corresponding residues on RBD of SARS-CoV. This would result in a reduced dissociation rate of SARS-CoV-2 from human receptor protein compared to SARS-CoV. The phenomenon was consistently observed in simulations beyond 500 ns and was reproducible across different force fields. Altogether, our study adds more insight into the critical dynamics of the key residues at the virus spike and human receptor binding interface and potentially aids the development of diagnostics and therapeutics to combat the pandemic efficiently.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

COVIDScholar: An automated COVID-19 research aggregation and analysis platform

The ongoing COVID-19 pandemic produced far-reaching effects throughout society, and science is no exception. The scale, speed, and breadth of the scientific community’s COVID-19 response lead to the emergence of new research at the remarkable rate of more than 250 papers published per day. This posed a challenge for the scientific community as traditional methods of engagement with the literature were strained by the volume of new research being produced. Meanwhile, the urgency of response lead to an increasingly prominent role for preprint servers and a diffusion of relevant research through many channels simultaneously. These factors created a need for new tools to change the way scientific literature is organized and found by researchers. With this challenge in mind, we present an overview of COVIDScholar https://covidscholar.org/, an automated knowledge portal which utilizes natural language processing (NLP) that was built to meet these urgent needs. The search interface for this corpus of more than 260,000 research articles, patents, and clinical trials served more than 33,000 users at an average of 2,000 monthly active users and a peak of more than 8,600 weekly active users in the summer of 2020. Additionally, we include an analysis of trends in COVID-19 research over the course of the pandemic with a particular focus on the first 10 months, which represents a unique period of rapid worldwide shift in scientific attention.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Using intrahost single nucleotide variant data to predict SARS-CoV-2 detection cycle threshold values

Over the last four years, each successive wave of the COVID-19 pandemic has been caused by variants with mutations that improve the transmissibility of the virus. Despite this, we still lack tools for predicting clinically important features of the virus. In this study, we show that it is possible to predict the PCR cycle threshold (Ct) values from clinical detection assays using sequence data. Ct values often correspond with patient viral load and the epidemiological trajectory of the pandemic. Using a collection of 36,335 high quality genomes, we built models from SARS-CoV-2 intrahost single nucleotide variant (iSNV) data, computing XGBoost models from the frequencies of A, T, G, C, insertions, and deletions at each position relative to the Wuhan-Hu-1 reference genome. Our best model had an R 2 of 0.604 [0.593–0.616, 95% confidence interval] and a Root Mean Square Error (RMSE) of 5.247 [5.156–5.337], demonstrating modest predictive power. Overall, we show that the results are stable relative to an external holdout set of genomes selected from SRA and are robust to patient status and the detection instruments that were used. This study highlights the importance of developing modeling strategies that can be applied to publicly available genome sequence data for use in disease prevention and control.

COVID19↗

DOE OSTI2020

Dynamical model for social distancing in the U.S. during the COVID-19 epidemic

Social distancing has led to a “flattening of the curve” in many states across the U.S. This is part of a novel, massive, global social experiment which has served to mitigate the COVID-19 pandemic in the absence of a vaccine or effective anti-viral drugs. Hence it is important to be able to forecast hospitalizations reasonably accurately. We propose on phenomenological grounds a random walk/generalized diffusion equation which incorporates the effect of social distancing to describe the temporal evolution of the probability of having a given number of hospitalizations. The probability density function is log-normal in the number of hospitalizations, which is useful in describing pandemics where the number of hospitalizations is very high. We used this insight and data to make forecasts for states using Monte Carlo methods. Back testing validates our approach, which yields good results about a week into the future. States are beginning to reopen at the time of submission of this paper and our forecasts indicate possible precursors of increased hospitalizations. However, the trends we forecast for hospitalizations as well as infections thus far show moderate growth. Additionally we studied the reproducibility in New York (Italian strain) and California (Wuhan strain). We find that even if there is a difference in the transmission of the two strains, social distancing has been able to control the progression of COVID 19.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Additive manufacturing for COVID-19: Devices, materials, prospects, and challenges

The current COVID-19 pandemic has caused the shortage of personal protective equipment (PPE) where improvised manufacturing in particular 3D printing has addressed many needs. This prospective discusses the current global crisis, then follows the wide interest in addressing the shortage of medical devices and PPEs used for treatment and protection against pathogens. An overview of the 3D printing process with polymer materials is given followed by the different 3D printing projects of PPEs and medical devices that emerged for the pandemic (including validation/testing). The potential for rapid prototyping with different polymer materials and eventual high-throughput production is emphasized.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Protein Factory on a Chip for Rapid Therapeutics

The potential for new, re-emerging, or engineered pandemic threats could challenge our nation's biosecurity, requiring rapid and flexible countermeasure development platforms. In healthcare, one of the main hindrances to the timely development of protein-based therapeutics for cancer, infectious disease, and other diseases has been a slow and expensive quality-controlled production stage. We present a protein-based technology that has the potential to address these crucial problems in biosecurity and healthcare by maximizing protein production and system portability, while minimizing production latency and cost. Specifically, we have developed a reusable, portable first-generation platform that leverages the advantages of cell-free protein synthesis (CFPS) and flow cells to create practical amounts of clinically relevant therapeutics. We have demonstrated that this technology is reusable and programmable, enabling a single platform that can synthesize multiple dosages of various therapeutics and vaccine in the field. Implementing this technology could mitigate pandemic threats by providing a rapidly-deployable (just-in-time), versatile (precision medicine) production of biologic therapeutics to the infected patient’s locale (point-of-care). In drug development, once promising drug variations are identified, the platform can be scaled to quickly produce practical amounts of protein for patient testing in the matter of days (vs. months) at a fraction of the cost of existing technology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Bioscience COVID Rapid Response Report

The COVID-19 disease outbreak and its impact on global health and economies have highlighted the national security threat posed by pathogens with pandemic potential and the need for rapid development of effective diagnostics and medical countermeasures. The Bioscience IA selected for funding rapid COVID LDRD project proposals that addressed critical R&D gaps in pandemic response that could be accomplished in 1-3 months with the requested funding. In total, the Bioscience IA funded nine rapid projects that addressed 1) rapid and accurate methods for SARS-CoV-2 RNA detection, 2) modeling tools to help prioritize populations for diagnostic testing, 3) bioinformatic tools to track SARS-CoV-2 genomic sequence changes over time, 4) molecular inhibitors of SARS-CoV-2 cellular infection, and 5) method for rapid staging of COVID19 disease to enable administration of more effective treatments. In addition, LDRD funded one larger project to be completed in FY21 that leverages Sandia capabilities to address the need for platform diagnostics and therapeutics that can be rapidly tailored against emerging pathogen targets.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Powering Post-COVID-19 Resilient Recovery with Green Stimulus

As the coronavirus disease (COVID-19) pandemic spread globally in early 2020, many governments enacted lockdown measures as an initial containment response. These lockdowns, while effective in slowing infection rates, have also had substantial economic consequences. A series of supply and demand shocks, workforce and supply chain disruptions, and other impacts of the pandemic and ensuing lockdowns have upended the livelihoods for much of the world's population. This quick read spotlights how green stimulus plans and other recovery measures can support near-term recovery and enable longer-term power system resilience against future threats. The term “green stimulus” encompasses fiscal measures (i.e., governmental tax and spending actions) that support short-term economic activity that enhances environmental and natural resource quality over a longer term. The deployment of energy efficiency (EE) and renewable energy (RE) technologies, as well as associated initiatives (e.g., grid modernization, electrification of transport, and so on) have been demonstrably effective in providing this kind of short-run economic boost while simultaneously strengthening the resilience of the power sector. Along with green stimulus packages, concurrent action on the regulatory side—for example, the development of green building codes, updating grid interconnection standards, creating programs and marketplaces for demand-side efficiency, and others—can be a force multiplier in ensuring that public spending achieves maximum impact in near-term recovery and long-term power sector transformation

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Community Conversation [Slides]

Our employees are the reason we kept up so much vital work during the pandemic. LANL is responsible for important R&D work for the nation and our scope is growing. COVID has taught us there are many ways to leverage technology to work effectively. Our researchers are making important contributions to nuclear security, climate change, environmental remediation, and fuel cell technology. LANL played a key leadership role in the pandemic by identifying mutations, predicting spread, and providing decision support. LANL has exciting news and changes on the horizon.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2021

COVID-19, An Exercise in Data Governance at Sandia National Laboratories

In April of 2020, Sandia National Laboratories had an urgent need to identify and manage the data that could be used to create mobile applications, models, reports, and visualizations to assist management in safely bringing the workforce onsite during the COVID-19 pandemic. Multiple divisions volunteered to design and build software solutions; meanwhile, requests for new data sources, including duplicate requests, were inundating Information Technology (IT) and data owners. The Enterprise Data Governance Team was assigned to resolve obtaining and accessing new sources of data in an accelerated timeframe. Through successful collaboration with multiple stakeholders and domain owners across Sandia, the Enterprise Data Governance Team rapidly developed a centralized data strategy and solution for use in safeguarding the Sandia workforce during the COVID-19 pandemic. This foundation enabled teams to successfully develop solutions, including reports for executives and management as well as the data for modeling and scientific analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2021

The Vault National Security Then & Now (2021 Annual)

Looking back, I can say with certainty that we rose to the challenge. Like other entities all around the globe, the National Security Research Center was tested by the COVID-19 pandemic. As a young organization (we celebrated our second anniversary in June 2021) and one that is critical to Los Alamos National Laboratory’s national security charge, we faced the difficult task of continuing our mission-essential work while keeping our staff safe. The NSRC is one of the largest libraries in the United States, and it houses the country’s most-comprehensive collections of nuclear weapons-related national security material. The NSRC contains millions of documents, films, photos, and other materials on the development, testing, and production of nuclear weapons. These are accessed on a daily basis by scientists, engineers, and researchers at Los Alamos and other National Nuclear Security Administration’s labs and sites, as well as partners in the Department of Defense. It was not an option for us to close our doors - our nation’s security depended on us not to. Not only did we continue operating under new safety protocols, we successfully accomplished many pre-pandemic goals. Thanks to the NSRC’s dedicated staff, our accomplishments during our second year are too numerous to list, though two in particular come to mind.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2021

A targeted opsonization platform for programming innate immunity against rapidly evolving novel viruses.

Recent work has shown that artificial opsonins stimulate the targeted destruction of bacteria by phagocyte immune cells. Artificial opsonization has the potential to direct the innate immune system to target novel antigens, potentially even viral pathogens. Furthermore, the engagement of innate immunity presents a potential solution for the spread of pandemics in a scenario when a vaccine is unavailable or ineffective. Funded by the LDRD late start bioscience pandemic response program, we tested whether artificial opsonins can be developed to target viral pathogens using phage MS2 and a SARS-CoV-2 surrogate. To direct opsonization against these viruses we purified antibody derived viral targeting motifs and attempted the same chemical conjugation strategies that produced bacterial targeting artificial opsonins. However, the viral targeting motifs proved challenging to conjugate using these methods, frequently resulting in precipitation and loss of product. Future studies may be successful with this approach if a smaller and more soluble viral-targeting peptide could be used.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

2021 Annual Report: Atmospheric Radiation Measurement

In fiscal year 2021 (FY2021), the COVID-19 pandemic continued to have an impact on activities within the Atmospheric Radiation Measurement (ARM) user facility. Travel was limited, which affected field activities and forced the continuation of virtual meetings. However, the ability to travel expanded significantly through the year, bringing some return to normalcy, and throughout the year, there was a great deal of activity dedicated to advancing the facility. Because of COVID, ARM twice delayed the TRacking Aerosol Convection interactions ExpeRiment (TRACER) in the Houston, Texas, area. Originally planned to launch in the spring, TRACER started October 1, 2021, one month after the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign began near Crested Butte, Colorado. ARM teams worked onsite and remotely to make sure both campaigns could launch on this new schedule that was set early in the year. Data from SAIL and TRACER will be critical to improving earth system models, each contributing to different sets of issues. SAIL measurements will provide insights into how precipitation forms and water travels through the Upper Colorado River Basin. TRACER scientists want to know whether tiny atmospheric particles can influence the severity of thunderstorms. In October 2020, the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition ended after 13 months. MOSAiC data from ARM and other organizations are already helping scientists better understand how ice, atmosphere, and ocean systems are connected in the central Arctic. This report discusses some early investigations from MOSAiC, along with other ARM campaigns and activities generating prolific research. In this report, you will also learn how research activities moved forward during the pandemic at ARM’s fixed-location atmospheric observatories. There were important science applications from measurements across the facility. ARM spent a significant amount of FY2021 looking back—and ahead. In November 2020, ARM had its Triennial Review. This review is held every three years to evaluate ARM’s effectiveness in science, operations, and management. Overall, the reviewers had positive feedback regarding the breadth and impact of science activities using ARM data and the way ARM strives to meet changing user needs. The reviewers also provided some recommendations for strengthening ARM going forward. Incorporating feedback from the review, we finalized an updated Decadal Vision document that will help guide ARM priorities in the coming years. This report describes the four themes driving ARM’s Decadal Vision.

54 ENVIRONMENTAL SCIENCES↗